Who is watching retail theft this holiday season?
Santa hats and holiday gifts are being stolen in plain sight – at both the manned and self-checkout. Overhead cameras at the checkout are detecting the growing theft of retail goods. But who’s watching the cameras?
Many retailers have implemented StopLift Checkout Vision Systems, whose technology detects unpaid merchandise on the conveyor belt, in the shopping cart, or reusable bag at both the manned and self-checkout. StopLift is a pioneer in detecting retail loss through “sweethearting,” when cashiers do not scan merchandise or charge the customer for it. The customer is often a friend, family member or fellow employee working in tandem with the cashier.
Click the below links to watch these real videos of holiday gifts being stolen:
1. Christmas toys given away without scanning
2. Giving away Santa hats without scanning
3. Four women at self-checkout with lots of scan-avoided Christmas gifts
“Retailers are counting on the holiday shopping season to boost slagging sales, and sweethearting theft is an even bigger problem now when stores are crowded and shoppers are impatient to get through the checkout,” said Malay Kundu, CEO of StopLift.
“Our technology has found that shoplifting is as much as 5 times more likely to happen in the self-checkout lane,” Kundu added.
StopLift’s “ScanItAll” video recognition technology detects the sweethearting of unscanned items. The technology, which works with existing overhead cameras, catches middle of the basket (MOB) and bottom of the basket (BOB) retail theft as well as items bypassed on the conveyor belt in both manned and self-checkouts.
Cashiers often overlook MOB and BOB items in the shopping cart – often deliberately. Reasons include sweethearting for friends and relatives as well as long lines of impatient shoppers – especially during holiday shopping, pressures on cashiers to work more quickly, and distracted cashiers or customers, particularly during holiday shopping.
Less than 1 percent of store video is actually viewed, according to Kundu. Thus, retailers have had no means of detecting MOB and BOB theft or sweethearting at the scanner.
The U.S. National Retail Federation states that about $14 billion of retail shrink is due to sweethearting.
StopLift’s computer vision technology visually determines what occurs during each and every transaction to immediately identify fraud at the checkout. In the process of monitoring100% of the security video, it flags the transaction as suspicious and quickly reports the incident, identifying the cashier and the date and time of the theft.
Dishonest associates are identified on the basis of video evidence the very first time they conduct a fraudulent transaction, rather than months or even years down the road, significantly reducing inventory shrinkage, deterring future theft, and boosting profitability.
The technology eliminates costly, time-consuming human review of video, drastically reduces and deters fraud at the checkout, and significantly improves profitability, Kundu said. Rather than take a one-size-fits-all approach, StopLift develops targeted applications to address the specific needs of retailers from different sectors including general merchandise, grocery, and specialty retail.
Retailers have traditionally tried to track loss at the checkout through data mining, but since MOB, BOB and other sweethearting involve items not being scanned, Kundu notes: “How do you do data mining when there’s no data?”
StopLift Checkout Vision Systems grew out of Kundu’s Harvard Business School research study “Project StopLift” on Retail Loss Prevention. With technological research insights Kundu developed while at MIT, Project StopLift concluded that video recognition could be used to automate and, thus, make possible the comprehensive examination of surveillance video. Prior to founding StopLift, Kundu developed facial recognition systems for identifying terrorists in airports.
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